Summary
Nowadays, concurrent participation of distributed energy resources in providing electrical energy and ancillary services brings many benefits. Also, energy storage systems (ESSs) along with renewable generations have a growing penetration rate in modern power systems aimed at declining environmental issues. Due to the uncertainties of intermittent nonpredictable power resources in microgrids (MGs), high integration of these products leads to an increase in ancillary services requirements and it necessitates the coordinated management of these technologies with the ESSs. In this paper, for the first time, a robust model based on particle swarm optimization (PSO) metaheuristic is developed to cope with the uncertainty of renewables in concurrent active/reactive and reserve management problem in the MG with ESS. The robust framework provides a medium priority in comparison with deterministic and stochastic techniques. The objective function of the robust concurrent active/reactive and reserve scheduling problem in MGs is expressed as maximizing social welfare (SW). The proposed model is carried out using a max‐min optimization scheme. The robust design will be attained in such a way that the maximizer at the outer level seeks an optimal solution against the worst‐case objective function achieved through the minimizer at the inner level taking into account the uncertainty neighborhood. The performance of the presented approach has been evaluated on a typical MG. Simulation results verify that the suggested robust‐PSO technique will aid MG operators to decrease daily operational costs and to yield a higher SW.
Background: In recent years, simultaneous participation in electrical energy and ancillary services markets has been very profitable for distributed energy resources (DERs). Moreover, the presence of renewable generations along with energy storage systems (ESS) is bringing a significant contribution to modern distribution systems. High penetration of non-predictable power sources in microgrids (MGs), due to the uncertainties of these products, increases the need for ancillary services and the management and coordination of these technologies combined with the ESSs. Results: For the first time, this paper develops a robust particle swarm optimization model to handle the uncertain renewable power production involved in the joint active/reactive and reserve scheduling of a smart MG. The robust optimization approach has a medium priority compared to deterministic and stochastic ones. The objective function utilized for the optimal joint active/reactive and reserve scheduling of an MG is defined as maximizing social welfare, which is accomplished based on a max-min optimization model. The robust optimal solution can be achieved in such a way that the maximizer at the outer level makes an optimal decision against the worst-case objective function, which is acquired based on the minimizer at the inner level considering the uncertainty neighborhood. Conclusions: The effectiveness of the proposed method is examined on a 33-bus MG test system. Simulation results prove that the proposed RPSO model can help MG operators to reduce scheduling costs to obtain a higher social welfare. The consideration of more uncertainty in renewable energy resources production leads to higher operation costs, especially reserve costs. Integration of robustness against uncertainty in the joint active/reactive and reserve management in the smart MGs leads to a more robust operation at the expense of higher costs.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.